1. Field of Invention
The field of the currently claimed embodiments of this invention relates to systems and methods for detection tissue abnormalities, and more particularly to systems and methods for detection tissue abnormalities from image data.
2. Discussion of Related Art
Multiple sclerosis (MS) is an inflammatory disease of the central nervous system, characterized by brain and spinal cord lesions. Although lesions in the gray matter of the brain are common, lesions are more readily recognized in the white matter (Sahraian et al., 2007). Magnetic resonance imaging (MRI) of the brain is used to detect lesions in MS and is essential in diagnosis and monitoring disease progression. In addition to documenting disease effects at one time, MRI can be used to assess longitudinal changes (Duan et al., 2008). MRI observation of lesion volume change resulting from the development of new lesions, enlarging lesions, and resolving lesions is an important marker of disease progression and response to therapy (Filippi et al., 2011). Lesion volume change is a common outcome in clinical trials and is computed by comparing radiologist manual segmentations of serial T2-weighted MRI (Simon et al., 2006), which is time consuming and costly. Also, quantifying lesion change is challenging because such lesions represent only a small proportion of all lesions, typically on the order of 5-10% (Tan et al., 2002).
Images from consecutive studies can be registered, normalized and then subtracted to isolate areas of lesion change. In these subtraction images, radiologically stable disease related measurements are cancelled. Manually segmented two-dimensional T2-weighted subtraction images identify a higher number of active lesions with greater inter- and intra-observer agreement (Tan et al., 2002) (Moraal et al., 2008) than comparing independently segmented serial images. However, these images are prone to artifacts from misregistration and partial volume effects (Duan et al., 2008). Subtraction images created from three-dimensional imaging acquisitions have better image quality and are less susceptible to registration errors. Subtraction images from various imaging modalities, such as three-dimensional double inversion-recovery, fluid-attenuated inversion recovery, T2-weighted, and T1-weighted volumes, have been shown to be less susceptible to the artifacts of two-dimensional subtraction images (Moraal et al., 2010). To our knowledge, no method for combining information from multiple modalities of MRI subtraction images has been developed.
Since subjects develop brain lesions over the natural course of multiple sclerosis, there is a need to identify, estimate the size of, and track, over the course of time, new lesions as they are being formed and remain in the brain. Currently, this is done manually by a trained neuroradiologist using slice-by-slice visual inspection. This process is very slow, can be prone to human error, and makes large observational studies of lesion development costly. Therefore, there remains a need for improved systems and methods for detection tissue abnormalities.